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FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration

  • Zeyu Xia
  • Lexie Chen
  • Ye Liu
  • Huilin Huang
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Abstract

Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis.

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Cite

BibTeXRIS
BibTeX
@misc{xia2026firedataforge,
  title = {{FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration}},
  author = {Zeyu Xia and Lexie Chen and Ye Liu and Huilin Huang},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2606.21198},
  eprint = {2606.21198},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.21198},
}
RIS
TY  - UNPB
TI  - FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration
AU  - Zeyu Xia
AU  - Lexie Chen
AU  - Ye Liu
AU  - Huilin Huang
PY  - 2026
DA  - 2026/09/18
PB  - arXiv
DO  - 10.48550/arxiv.2606.21198
UR  - https://arxiv.org/abs/2606.21198
AB  - Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis.
ER  -